Overview

Dataset statistics

Number of variables8
Number of observations27502
Missing cells0
Missing cells (%)0.0%
Duplicate rows29
Duplicate rows (%)0.1%
Total size in memory1.7 MiB
Average record size in memory64.0 B

Variable types

NUM8

Warnings

Dataset has 29 (0.1%) duplicate rows Duplicates
DayLength(s) is highly correlated with DayofYearHigh correlation
DayofYear is highly correlated with DayLength(s)High correlation
Speed has 403 (1.5%) zeros Zeros

Reproduction

Analysis started2020-09-19 22:12:16.273481
Analysis finished2020-09-19 22:12:35.955045
Duration19.68 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Radiation
Real number (ℝ≥0)

Distinct10894
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.9496975
Minimum1.11
Maximum883.66
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:36.045460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile1.19
Q11.22
median1.47
Q3219.9425
95-th percentile756.716
Maximum883.66
Range882.55
Interquartile range (IQR)218.7225

Descriptive statistics

Standard deviation249.1978092
Coefficient of variation (CV)1.673033336
Kurtosis1.196441045
Mean148.9496975
Median Absolute Deviation (MAD)0.3
Skewness1.590172796
Sum4096414.58
Variance62099.54812
MonotocityNot monotonic
2020-09-19T15:12:36.223005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.2221407.8%
 
1.2319917.2%
 
1.2119066.9%
 
1.2416095.9%
 
1.214605.3%
 
1.2510894.0%
 
1.199053.3%
 
1.266832.5%
 
1.184371.6%
 
1.274151.5%
 
Other values (10884)1486754.1%
 
ValueCountFrequency (%) 
1.111< 0.1%
 
1.134< 0.1%
 
1.144< 0.1%
 
1.15340.1%
 
1.16730.3%
 
ValueCountFrequency (%) 
883.661< 0.1%
 
883.591< 0.1%
 
883.451< 0.1%
 
883.431< 0.1%
 
883.371< 0.1%
 

Temperature
Real number (ℝ≥0)

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.47796524
Minimum36
Maximum68
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:36.443351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile44
Q146
median49
Q354
95-th percentile61
Maximum68
Range32
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.476834358
Coefficient of variation (CV)0.1084995073
Kurtosis-0.1143470163
Mean50.47796524
Median Absolute Deviation (MAD)3
Skewness0.6338528336
Sum1388245
Variance29.99571459
MonotocityNot monotonic
2020-09-19T15:12:36.576511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
4526599.7%
 
4824839.0%
 
4723358.5%
 
4620397.4%
 
5019507.1%
 
4918496.7%
 
5117706.4%
 
5214105.1%
 
4411724.3%
 
5311594.2%
 
Other values (23)867631.5%
 
ValueCountFrequency (%) 
36150.1%
 
37380.1%
 
38240.1%
 
39370.1%
 
401530.6%
 
ValueCountFrequency (%) 
68310.1%
 
67350.1%
 
66750.3%
 
651160.4%
 
641900.7%
 

Pressure
Real number (ℝ≥0)

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.43094829
Minimum30.31
Maximum30.55
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:36.709080image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum30.31
5-th percentile30.35
Q130.41
median30.43
Q330.46
95-th percentile30.49
Maximum30.55
Range0.24
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.04165413148
Coefficient of variation (CV)0.001368808197
Kurtosis0.4104691043
Mean30.43094829
Median Absolute Deviation (MAD)0.03
Skewness-0.4887204442
Sum836911.94
Variance0.001735066669
MonotocityNot monotonic
2020-09-19T15:12:36.851585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
30.44294110.7%
 
30.43282610.3%
 
30.4527079.8%
 
30.4225389.2%
 
30.4624278.8%
 
30.4120567.5%
 
30.4719537.1%
 
30.417796.5%
 
30.4814335.2%
 
30.3913474.9%
 
Other values (15)549520.0%
 
ValueCountFrequency (%) 
30.312771.0%
 
30.322921.1%
 
30.332530.9%
 
30.344021.5%
 
30.352911.1%
 
ValueCountFrequency (%) 
30.55310.1%
 
30.54750.3%
 
30.53550.2%
 
30.521590.6%
 
30.512600.9%
 

Humidity
Real number (ℝ≥0)

Distinct94
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.83913897
Minimum8
Maximum103
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:36.999934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile25
Q157
median87
Q398
95-th percentile102
Maximum103
Range95
Interquartile range (IQR)41

Descriptive statistics

Standard deviation26.17651351
Coefficient of variation (CV)0.345158369
Kurtosis-0.7036660498
Mean75.83913897
Median Absolute Deviation (MAD)14
Skewness-0.8270219108
Sum2085728
Variance685.2098597
MonotocityNot monotonic
2020-09-19T15:12:37.163308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10219647.1%
 
10118956.9%
 
9314915.4%
 
10014025.1%
 
9911114.0%
 
988553.1%
 
977252.6%
 
966512.4%
 
956352.3%
 
945071.8%
 
Other values (84)1626659.1%
 
ValueCountFrequency (%) 
81< 0.1%
 
114< 0.1%
 
1211< 0.1%
 
13330.1%
 
14360.1%
 
ValueCountFrequency (%) 
103930.3%
 
10219647.1%
 
10118956.9%
 
10014025.1%
 
9911114.0%
 

WindDirection(Degrees)
Real number (ℝ≥0)

Distinct15152
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.4715181
Minimum0.09
Maximum324.93
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:37.333885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile18.721
Q184.5
median143.88
Q3172.86
95-th percentile236.887
Maximum324.93
Range324.84
Interquartile range (IQR)88.36

Descriptive statistics

Standard deviation66.00487201
Coefficient of variation (CV)0.4982570817
Kurtosis0.1717425168
Mean132.4715181
Median Absolute Deviation (MAD)36.77
Skewness0.110164165
Sum3643231.69
Variance4356.643129
MonotocityNot monotonic
2020-09-19T15:12:37.487928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.11630.2%
 
0.1380.1%
 
85.39250.1%
 
0.12200.1%
 
25.99160.1%
 
191.13140.1%
 
85.412< 0.1%
 
153.2512< 0.1%
 
178.2512< 0.1%
 
183.0111< 0.1%
 
Other values (15142)2727999.2%
 
ValueCountFrequency (%) 
0.092< 0.1%
 
0.1380.1%
 
0.11630.2%
 
0.12200.1%
 
0.135< 0.1%
 
ValueCountFrequency (%) 
324.931< 0.1%
 
324.882< 0.1%
 
324.872< 0.1%
 
324.851< 0.1%
 
324.711< 0.1%
 

Speed
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.017859792
Minimum0
Maximum14.62
Zeros403
Zeros (%)1.5%
Memory size214.9 KiB
2020-09-19T15:12:37.639224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.12
Q13.37
median5.62
Q37.87
95-th percentile11.25
Maximum14.62
Range14.62
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.996703486
Coefficient of variation (CV)0.4979683125
Kurtosis-0.1790622997
Mean6.017859792
Median Absolute Deviation (MAD)2.25
Skewness0.4021007752
Sum165503.18
Variance8.980231782
MonotocityNot monotonic
2020-09-19T15:12:37.765057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
5.62395914.4%
 
4.5386114.0%
 
6.75372413.5%
 
3.37335512.2%
 
7.87306011.1%
 
2.2523698.6%
 
921397.8%
 
10.1214505.3%
 
1.1211644.2%
 
11.259023.3%
 
Other values (4)15195.5%
 
ValueCountFrequency (%) 
04031.5%
 
1.1211644.2%
 
2.2523698.6%
 
3.37335512.2%
 
4.5386114.0%
 
ValueCountFrequency (%) 
14.621910.7%
 
13.53651.3%
 
12.375602.0%
 
11.259023.3%
 
10.1214505.3%
 

DayofYear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct119
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.928987
Minimum245
Maximum366
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:37.907968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum245
5-th percentile251
Q1277
median305
Q3331
95-th percentile360
Maximum366
Range121
Interquartile range (IQR)54

Descriptive statistics

Standard deviation33.90880953
Coefficient of variation (CV)0.1112023159
Kurtosis-1.064740558
Mean304.928987
Median Absolute Deviation (MAD)27
Skewness0.04794382677
Sum8386157
Variance1149.807364
MonotocityNot monotonic
2020-09-19T15:12:38.079735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3562841.0%
 
3372841.0%
 
3292831.0%
 
3592821.0%
 
3032811.0%
 
3272791.0%
 
3282781.0%
 
3342771.0%
 
3572771.0%
 
3632771.0%
 
Other values (109)2470089.8%
 
ValueCountFrequency (%) 
2451000.4%
 
2462150.8%
 
2472530.9%
 
2482631.0%
 
2492360.9%
 
ValueCountFrequency (%) 
3661480.5%
 
3651360.5%
 
3642731.0%
 
3632771.0%
 
3622661.0%
 

DayLength(s)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct75
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41495.97557
Minimum39360
Maximum45060
Zeros0
Zeros (%)0.0%
Memory size214.9 KiB
2020-09-19T15:12:38.244285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum39360
5-th percentile39360
Q139840
median41160
Q342900
95-th percentile44760
Maximum45060
Range5700
Interquartile range (IQR)3060

Descriptive statistics

Standard deviation1774.647417
Coefficient of variation (CV)0.04276673564
Kurtosis-1.109184696
Mean41495.97557
Median Absolute Deviation (MAD)1500
Skewness0.4303165498
Sum1141222320
Variance3149373.455
MonotocityNot monotonic
2020-09-19T15:12:38.396904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39360299610.9%
 
3942013625.0%
 
396005712.1%
 
399005512.0%
 
400205492.0%
 
403205462.0%
 
411605432.0%
 
400805422.0%
 
428405361.9%
 
413405321.9%
 
Other values (65)1877468.3%
 
ValueCountFrequency (%) 
39360299610.9%
 
3942013625.0%
 
394802350.9%
 
395402901.1%
 
396005712.1%
 
ValueCountFrequency (%) 
450602020.7%
 
450002210.8%
 
448802610.9%
 
448204691.7%
 
447602300.8%
 

Interactions

2020-09-19T15:12:23.067460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:23.284044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:23.456993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:23.649531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:23.802464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:23.945711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:24.107847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:24.251036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:24.406174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:24.559844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:24.722610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:24.893166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:25.049360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:25.213169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:25.395370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:25.551545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:25.773636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:25.957583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:26.132410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:26.291784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:26.454341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:26.609015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:26.779189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:26.933343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:27.085892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:27.239653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:27.400904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:27.554524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:27.697346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:27.869351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.014556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.165900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.317493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.472933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.635353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.797209image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:28.959783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:29.141637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:29.329428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:29.489432image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:29.662904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:29.815880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:29.990112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:30.152397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:30.375719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:30.539325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:30.711401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:30.915350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:31.097816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:31.250683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:31.414182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:31.574142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:31.750163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:31.942713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:32.124391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:32.289568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:32.463479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:32.606321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:32.768119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:32.924608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:33.081386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:33.234611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:33.398106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:33.563352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-19T15:12:38.654306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-19T15:12:38.875618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-19T15:12:39.112844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-19T15:12:39.337014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-19T15:12:33.857734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-19T15:12:34.246946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

RadiationTemperaturePressureHumidityWindDirection(Degrees)SpeedDayofYearDayLength(s)
01.214830.4659177.395.6227443200
11.214830.4658176.783.3727443200
21.234830.4657158.753.3727443200
31.214830.4660137.713.3727443200
41.174830.4662104.955.6227443200
51.214830.4664120.205.6227443200
61.204930.4672112.456.7527443200
71.244930.4671122.975.6227443200
81.234930.4680101.184.5027443200
91.214930.4685141.874.5027443200

Last rows

RadiationTemperaturePressureHumidityWindDirection(Degrees)SpeedDayofYearDayLength(s)
274921.204330.42101150.626.7533639660
274931.194330.42101178.465.6233639660
274941.194430.42101148.633.3733639660
274951.214430.42101101.2710.1233639660
274961.184430.42102121.024.5033639660
274971.224430.43102145.426.7533639660
274981.174430.42102117.786.7533639660
274991.204430.42102145.199.0033639660
275001.234430.42101164.197.8733639660
275011.204430.4310183.593.3733639660

Duplicate rows

Most frequent

RadiationTemperaturePressureHumidityWindDirection(Degrees)SpeedDayofYearDayLength(s)count
151.284930.4110285.390.00346394204
01.214130.3289188.965.62365394203
21.224530.3293211.821.12339396603
11.214730.4490166.761.12280427202
31.234530.31102162.872.25365394202
41.234530.3593238.132.25340396002
51.234530.3693188.381.12340396002
61.234830.4399208.974.50259441602
71.234930.4496283.212.25259441602
81.235030.4598179.173.37268435002